Advertisement

CentroidNet: A Deep Neural Network for Joint Object Localization and Counting

  • K.  DijkstraEmail author
  • J. van de Loosdrecht
  • L. R. B. Schomaker
  • M. A. Wiering
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)

Abstract

In precision agriculture, counting and precise localization of crops is important for optimizing crop yield. In this paper CentroidNet is introduced which is a Fully Convolutional Neural Network (FCNN) architecture specifically designed for object localization and counting. A field of vectors pointing to the nearest object centroid is trained and combined with a learned segmentation map to produce accurate object centroids by majority voting. This is tested on a crop dataset made using a UAV (drone) and on a cell-nuclei dataset which was provided by a Kaggle challenge. We define the mean Average F1 score (mAF1) for measuring the trade-off between precision and recall. CentroidNet is compared to the state-of-the-art networks YOLOv2 and RetinaNet, which share similar properties. The results show that CentroidNet obtains the best F1 score. We also explicitly show that CentroidNet can seamlessly switch between patches of images and full-resolution images without the need for retraining.

References

  1. 1.
    Cheema, G.S., Anand, S.: Automatic detection and recognition of individuals in patterned species. In: Altun, Y., et al. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10536, pp. 27–38. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-71273-4_3CrossRefGoogle Scholar
  2. 2.
    Cohen, J.P., Boucher, G., Glastonbury, C.A., Lo, H.Z., Bengio, Y.: Count-ception: counting by fully convolutional redundant counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 18–26 (2017)Google Scholar
  3. 3.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2015)Google Scholar
  4. 4.
    Hsieh, M.R., Lin, Y.L., Hsu, W.H.: Drone-based object counting by spatially regularized regional proposal network. In: The IEEE International Conference on Computer Vision (ICCV), vol. 1 (2017)Google Scholar
  5. 5.
    Kainz, P., Urschler, M., Schulter, S., Wohlhart, P., Lepetit, V.: You should use regression to detect cells. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 276–283. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_33CrossRefGoogle Scholar
  6. 6.
    Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Conference on Computer Vision and Pattern Recognition, vol. 1, p. 4 (2017)Google Scholar
  7. 7.
    Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. arXiv preprint arXiv:1708.02002 (2017)
  8. 8.
    Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_2CrossRefGoogle Scholar
  9. 9.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2014)Google Scholar
  10. 10.
    Milletari, F., et al.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput. Vis. Image Underst. 164, 92–102 (2017)CrossRefGoogle Scholar
  11. 11.
    Nguyen, H.T.H., Wistuba, M., Schmidt-Thieme, L.: Personalized tag recommendation for images using deep transfer learning. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10535, pp. 705–720. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-71246-8_43CrossRefGoogle Scholar
  12. 12.
    Pietikaäinen, M.: Computer Vision Using Local Binary Patterns. Springer, London (2011).  https://doi.org/10.1007/978-0-85729-748-8CrossRefGoogle Scholar
  13. 13.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 779–788 (2016)Google Scholar
  14. 14.
    Redmon, J., Farhadi, A.: YOLO9000: Better. Stronger. arXiv preprint, Faster (2017)Google Scholar
  15. 15.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2015)CrossRefGoogle Scholar
  16. 16.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  17. 17.
    Szegedy, C., et al.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition (CVPR) (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • K.  Dijkstra
    • 1
    • 2
    Email author
  • J. van de Loosdrecht
    • 1
  • L. R. B. Schomaker
    • 2
  • M. A. Wiering
    • 2
  1. 1.Centre of Expertise in Computer Vision and Data ScienceNHL Stenden University of Applied SciencesLeeuwardenNetherlands
  2. 2.Department of Artificial Intelligence, Bernoulli InstituteUniversity of GroningenGroningenNetherlands

Personalised recommendations